The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Magnetic Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there is a need for developing image processing techniques that automatically determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. First concern is related to the accuracy of recognition. Another concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed of recognition is of utmost important to aid the doctor in reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a 2-D image made of pixel elements or a 3-D image made of volume elements (“voxels”) . Such 2-D or 3-D images are processed using medical image recognition techniques to determine presence of anatomical structures such as cysts, tumors, polyps, etc. However, given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Feature based recognition techniques are used to determine presence of anatomical structures in medical images. However, feature based recognition techniques suffer from accuracy problems. Hence, there is a need for non-feature based recognition techniques that provide improved recognition of anatomical features in medical images.